Jianzhong Su

Huntingtin (HTT) is a naturally aggregating protein that causes Huntington’s disease (HD) when its polyglutamine (polyQ) tract exceeds 38 repeats. Despite its importance, the biology of HTT aggregates remains poorly defined. Utilizing high-resolution imaging of HD family-derived cells, we have redefined polyQ assemblies—formerly viewed as pathogenic aggregates—as dynamic structures resembling knitted-fabric patches. These assemblies encircle the Golgi apparatus, integrating ribbons and stacks to form a functional polyQ assembly-Golgi complex and attaching clathrin vesicles. Mechanistically, we show that the fragmentation of polyQ assemblies is dynamically coupled with mitotic Golgi fragmentation and that treatment with the ARF inhibitor Brefeldin A splits and fragments the complex. The presence of mutant HTT (mHTT) ’crisps’ these assemblies and complexes, altering their response to nutrient deprivation and autophagy enhancers but not to antisense oligonucleotide (ASO) therapy. The polyQ assembly in HD cells also reduces the scaffolding capacity of the Golgi apparatus, clathrin vesicles, and ARF1, impairing Golgi functions. Our results demonstrate that mHTT disrupts the homeostatic dynamics of the polyQ assembly-Golgi complex, inducing a ’Golgipathy’ by crippling Golgi structure and function.

The global epidemic of myopia constitutes a growing public health concern worldwide. Myopia development is characterized by pathological scleral remodeling through fibroblast-myofibroblast transdifferentiation (FMT) and extracellular matrix (ECM) degradation. Since myopia is progressive, the development of sustainable and safe preventive interventions is imperative. While mitochondrial dynamics critically regulate fibrotic processes in other organs, their role in scleral homeostasis has remained unexplored. Here, we identify pathological mitochondrial fragmentation, caused by increased mitochondrial fission, as a key driver of myopia progression. Using two mammalian animal models, we demonstrate that both genetic and pharmacological enhancement of mitochondrial fission (inducing mitochondrial fragmentation) exacerbates collagen loss and accelerates axial elongation, whereas genetic and pharmacological inhibition of mitochondrial fission prevents collagen degradation and attenuates myopia progression. Hypoxia-induced FMT in cultured human scleral fibroblasts (HSFs) requires activation of mitochondrial fission, revealing overproduction of reactive oxygen species (ROS) as the downstream effector on HSFs and in both animal models. Our multilevel analyses identify the mitochondrial fission-ROS axis as a key pathway linking scleral hypoxia to ECM remodeling. Lycopene, a naturally occurring carotenoid antioxidant, significantly attenuated scleral ROS levels and was found suitable for long-term application, highlighting its potential as a therapeutic agent for myopia control. Collectively, these findings have identified a therapeutic target and agent for controlling myopia progression.

Zebrafish (Danio rerio) ocular measurements are widely used in ocular disease research and drug discovery. Traditional methods rely on manual annotations, causing challenges in precision, efficiency, and workload. Here, we introduce a deep learning-based zebrafish ocular morphometric analysis (Deep-ZOMA) tool for quantitative measurement of zebrafish larvae ocular structures. A dual-center dataset of 1820 bright-field images (1010 internal; 810 external) was annotated for key ocular regions. A UNet++ segmentation network was trained with augmentation and hybrid loss. Sixteen morphometric parameters were computed using customized quantification algorithms. Performance was evaluated on internal and external datasets using Dice coefficient, IoU, Student's t-test, correlation, and agreement analyses. Utility was further tested on an slc4a7 knockdown/rescue dataset and compared with three ophthalmology graduate students. Deep-ZOMA achieved mean Dice coefficients of 0.96 on the internal set and 0.95 on the external set for primary ocular regions, with IoU values >0.90. Automated measurements showed strong correlations and excellent agreement with expert measurements. In the slc4a7 model, Deep-ZOMA accurately identified microphthalmia and rescue phenotypes consistent with expert annotations. Measurement and data-entry times were >20-fold faster than those of human observers, with comparable or better accuracy. Deep-ZOMA provides a reliable and efficient solution for high-throughput zebrafish ocular morphometry, supporting applications in ocular genetics, drug screening, and phenotypic studies. This deep learning-based system enables accurate, reproducible zebrafish ocular morphometry, accelerating translational research by linking genetic or pharmacological perturbations with quantifiable ocular outcomes relevant to human eye diseases.

Myopia, particularly high myopia (HM), represents a critical global public health issue due to its strong association with irreversible visual impairment. While past genetic studies have identified numerous myopia-associated loci, they explain only a modest proportion of its heritability. To address this gap, we established the Myopia Associated Genetics and Intervention Consortium (MAGIC) cohort, the first large-scale whole-exome sequencing (WES) cohort dedicated to the Chinese myopia population, which has already unveiled HM-associated genes, novel rare-variant mechanisms, and open-access analytic tools. The cohort was established in Wenzhou, China, in May 2019 and comprises three phases. Phase I (n = 9696) enrolled extreme, early-onset HM adolescents; Phases II (n = 9368) and III (n = 3544; ongoing recruitment) include young adults and elders, enabling comparison and replication of genetic findings. Longitudinal data from electronic medical records (EMRs) for 2566 individuals further support progression analysis. Here, we extend the MAGIC resource to dissect the genetic basis of myopia-related anisometropia. We discovered novel association signals for spherical-equivalent anisometropia in HM adolescents, including SNP rs61758755 in RAD23B (β = 0.72, P = 1.50 × 10−9) and SNP rs59002125 in ABCC6 (β = 0.69, P = 8.86 × 10−8). We also identified 26 suggestive variants linked to anisometropia and its progression. Together, these findings underscore the value of large-scale WES in detecting functional risk alleles and provide foundational insights into the genetic architecture of myopia and its relevant refractive error. The MAGIC cohort serves as a valuable foundation for future mechanistic studies.

Inherited genetic variation can weaken the ability of the immune system to detect and eliminate malignant cells, limiting the effectiveness of cancer immunotherapy. However, how germline polymorphisms shape the tumor immune microenvironment across cancers remains unclear. Here, we present a polygenic analysis framework that integrates single-cell RNA sequencing with GWAS summary statistics across 14 cancer types to identify genes, immune cells, and functional programs linked to cancer risk. We identify three major genetic modules associated with T cells, B cells, and myeloid cells, and show that the T-cell module is strongly linked to cytotoxic and regulatory T cells, particularly in melanoma and breast cancer. We also identify trait-related genes, including CST7, that are associated with cytokine signaling and antigen presentation. In addition, we develop a deep-learning model that predicts immunotherapy response from both tissue and blood samples, supporting the potential of integrated germline and immune features as predictive biomarkers. Together, these findings provide a framework for understanding how inherited variation shapes tumor immunity and may guide biomarker development for cancer immunotherapy.

Pigmentation varies widely across humans and is shaped by melanin quantity, type, and spatial distribution. Retinal pigmentation protects against light-induced damage, yet its genetic and evolutionary bases remain unclear. We developed a deep learning framework (DeepGRP) to quantify retinal pigmentation from high-resolution fundus images and conducted a genome-wide association study (GWAS), identifying 42 signals, including 26 previously unidentified loci, with single-nucleotide polymorphism–based heritability of 21.4%. Single-nucleus assay for transposase-accessible chromatin by sequencing and RNA sequencing of human fetal retinal tissues revealed key cellular contributors, including retinal pigment epithelium and photoreceptor cells. Among candidate genes, ARHGAP18 emerged as a previously unrecognized regulator of melanogenesis. Evidence of polygenic adaptation in Europeans suggests selection driven by snow-reflected light at high latitudes. A polygenic risk score for retinal pigmentation correlated with a 4.8-fold higher risk of myopia and a 1.5-fold lower risk of skin cancer. These findings demonstrate the power of deep learning for large-scale ocular phenotyping and reveal insights into the genetic and evolutionary architecture of retinal pigmentation.

Single-cell multiomics provides critical insights into how disease-associated variants identified through genome-wide association studies (GWASs) influence transcription factor eRegulons within a specific cellular context; however, the regulatory roles of genetic variants in aging and disease remain unclear. Here, we present scMORE, a method that integrates single-cell transcriptomes and chromatin accessibility with GWAS summary statistics to identify cell-type-specific eRegulons associated with diseases. scMORE effectively captures trait-relevant cellular features and demonstrates robust performance across simulated and real single-cell datasets, and GWASs for 31 immune- and aging-related traits, including Parkinson’s disease (PD). In the human midbrain, scMORE identifies 77 aging-relevant eRegulons implicated in PD across seven brain cell types and reveals sex-dependent dysregulation of these eRegulons in PD neurons compared to both young and aged groups. By linking genetic variation to cell type-resolved eRegulon activity, scMORE illuminates how variants shape trait-relevant regulatory networks and provides a practical framework for mechanistic interpretation of GWAS signals.

Supplementary Table S1 shows the high-confident NPM1c binding peaks in OCI-AML3 cells.